NDLI: Fusion of uncertain information using vague sets and Dempster-Shafer theories

Content Provider	IEEE Xplore Digital Library
Author	Bai Hua
Copyright Year	2009
Description	Author affiliation: School of Mechatronics Engineering, Harbin Institute of Technology, China (Bai Hua)
Abstract	During the process of fusing multi-source information, a challenging problem is how to deal effectively with the available data and information that are vague, imprecise and uncertain. Vague sets are suitable for accurately describing the uncertain information. Dempster-Shafer (D-S) theory is a promising method for the combination of evidence obtained from different source. A new approach is proposed by combining vague sets and D-S theory as there are some similarities between these two theories. By the use of vague sets, characteristics of the factors related to evaluate a component could be described in crisp form and can be represented using belief and plausibility functions defined in D-S theory. Then they are combined using an enhanced weight normalized combination algorithm based on D-S evidence combination rule to reduce the incompleteness and uncertainty. An example is conducted to demonstrate the effectiveness of the proposed method. According to its firm mathematical foundation, the proposed approach can be applied to any bodies of multi-source information fusion without changing the recursive combination algorithm.
Starting Page	17
Ending Page	22
File Size	532271
Page Count	6
File Format	PDF
ISBN	9781424441143
DOI	10.1109/IRI.2009.5211616
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Uncertainty Mechatronics Data engineering Fuzzy set theory Upper bound Sensor fusion Sensor phenomena and characterization Application software Biomedical monitoring Dempster-Shafer theory information fusion uncertainty vague sets
Content Type	Text
Resource Type	Article

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